Digital geospatial production processes today are highly influenced by analog processes before computers. In general, they are digital versions of analog processes that fail to leverage technologies to process information digitally in a way conducive to modern computer environments.
The present state of the art in photogrammetric production utilizes product-centric approaches that focus on imagery collected with a fixed spatial and temporal extent. A typical flow is set forth below:
1) Imagery is collected. The imagery may go to one or more libraries for storage. These libraries are geographically separated, and may be network-topologically separated, as well. For instance, each commercial imagery provider has its own storage, and some imagery may be duplicated on Government libraries at various security levels. Each image will have image support metadata containing sensor model parameters and often error estimates.
2) Image measurements of tie-points and control points are made. Additional imagery must be accessed to measure tie-points and control points. Sometimes tie-points or control points have been previously measured on the imagery. These measurements are performed in “image space.”
3) A photogrammetric triangulation using weighted least squares (WLS) adjustment is run. This adjusts the images for consistency and seamlessness over the project area.
4) Triangulation results are reviewed for quality. Triangulation is the process of improving the accuracy of imagery through measurements and mathematical computations. This is typically performed using a bundle block adjustment process.
5) Final adjustments are applied to imagery support metadata and tie point coordinates. This can be in the form of changes to initial or previous sensor model parameter values or in the form of additional sensor or geometry model parameters. The process often includes the update of error estimate information for the sensor model parameters and the tie points used in the triangulation process.
6) Derivative products are made. These include stereo products, such as Digital Point Positioning Data Base (DPPDB), monoscopic products, such as Controlled Image Base (CIB) and other orthorectified products, elevation data, such as Digital Terrain Elevation Data (DTED), and feature vector data.
Derivative products are made in “ground space” that represents the 3-D world. Elevation data can be extracted directly in 3-D using stereo imagery products. Orthorectified products are inherently 2-D and provide only horizontal ground coordinates although elevation data in the vertical dimension is required for the orthorectification process. Feature vector data can be extracted directly in 3-D using stereo imagery or in 2-D from orthorectified products with the third dimension applied from elevation data.
For the present state of the art, non-imagery derivative product data consists fundamentally of ground points with only ground point coordinates. Elevation data posts or Triangulated Irregular Network (TIN) points are points. Feature vector data consists of individual points and multiple-vertex lines, polygons, and 3-D volumes. The vertexes are points that connect the lines, polygons, and volumes. For the present state of the art, only the 2-D or 3-D ground coordinates of the points and vertexes of extracted data are stored. Even though imagery is used in the extraction process, line and sample coordinates of the points and vertexes as they appear in the image are not recorded.
A fair amount of manual labor is required in the production process. Automated methods still require editing by humans. In the current state of the art, these edits are performed only in “ground space” and the edits are not recorded or maintained for re-use and for use in “image space” with subsequent images. For instance, Digital Elevation Model (DEM) editing may involve tasks such as flattening lakes, ensuring that rivers run monotonically downstream, and defining breaklines on ridge tops. Even though these edits involve feature vector-like extractions of lines and polygons, they are not retained and so they cannot be re-applied to a DEM re-computed over the same area. Even if they were retained, only “ground space” coordinates are available and the process does not involve the connection of edits in “image space” to subsequent images that may be used to refine or update the DEM over the area.
The “product approach” is inherently inefficient. For a particular product, the imagery is triangulated using only the imagery collected for that product, such as 1° by 1° cells, then the information used to perform the triangulation (referred to herein as a part of “heritage information”) is either 1) not retained in the first place, 2) is gathered but thrown away upon process completion, or 3) is kept in the hands of a select few in one small part of the overall process. This heritage information has value and can be combined with other similar information to improve the accuracy of “product” imagery, as well as future and past imagery collected over the same area.
“Products” are produced with a certain accuracy requirement—either a product specification or via more stringent production guidelines. The areas typically are collected in a systematic fashion both spatially and temporally, and the images are grouped by convenient product sets, such as a 1°×1° cells, quadrangles, or counties. All of the imagery is collected for the “product” set before the rest of the workflow is started. Tie-points are collected in well-defined and regular overlap areas and triangulation (optionally with ground control points) is performed on all of the imagery in the product set at one time.
At periodic intervals, new imagery is collected and the entire production process is repeated. This is because, in the present state of the art, only the ground coordinates for the feature vectors and DEMs from the previous production iteration over the area are stored. The linkages from the image measurements through the triangulation process to product derivation and extraction are not maintained. There is no choice but to start the process anew to create new ground coordinates for image products, feature vectors, and DEMs. Either the old data is completely discarded and replaced with the new data, or an attempt is made to conflate the ground coordinates from the older and new data, which is a difficult process. New imagery is collected and an entirely new triangulation is performed. New derived data such as DEMs and feature vectors are re-extracted from the new imagery. Although some extraction can be automated, it still requires labor-intensive manual intervention. Current methods do not leverage off the labor put into the production of previous DEMs and vectors, including their editing. Instead labor is expended to re-extract vectors and DEMs for the same features and areas. The older vectors and DEMs are either discarded or even more labor is expended attempting to conflate the older and newer data. These architectures are highly centralized by nature, with one organization performing almost all of the work for a given product set. Quality control of these products relies upon processes designed to achieve confidence that the output is better than the requirement or specification rather than to achieve best accuracy and to quantify that accuracy.
What is needed is a system that values not only the raw image data and information extracted from the raw image data but the “heritage” information as well. This system can maintain the connection between “image space” and “ground space” not just for the images, but also for all information extracted from the images, through the heritage information. Extracted information includes elevation data, feature vector data, edits performed to these derived data, and the object-oriented properties, sometimes called attributes, of the derived data. The line and sample coordinates for everything extracted must be maintained with linkages to the image identifiers of the images used. The image identifiers are also linked to the imagery sensor model parameters for each image. Error estimate information is maintained for all line/sample coordinates, image sensor model parameters, and ground points and is used in the triangulation process. This allows re-derivation of geospatial coordinates at any time in the future.
When necessary, photogrammetric weighted least squares (WLS) re-adjustments are run. The imagery is re-triangulated in the traditional sense, but all other extracted information is also placed in the WLS process as an expansion of the traditional triangulation process in order to re-compute the ground coordinates of the extracted information. In this way, all extracted information, including DEM edits, is carried along with re-adjustments of the imagery. In a way, feature vector points and vertexes can function as additional tie points for computational purposes, but they continue to be treated as feature vectors in the traditional sense after the re-adjustment.
Additional constraints can be included based upon feature object properties in the WLS adjustments. For instance, constraints can be used to ensure corners of rooftops remain squared for the computation of ground coordinates for the roof corners.
Heritage information can also consist of known relationships between visible and non-visible objects. By way of example but not limitation, non-visible underground electrical lines can be geometrically connected to visible ground-level transformer boxes. When the transformer boxes are adjusted by the process, the position of the underground electrical line is updated by maintaining the known relative geometric relationship between the boxes and the line.
The new system supports a single geospatial object-oriented database that spans time and space. Features or objects acquire identity that is not tied to a single geographic point, but is instead associated with the best available knowledge about where they are located at any point in time. By way of example, but not limitation, instead of a single light house being stored redundantly in separate databases—as a point in a nautical database, as a vertical obstruction in an aeronautical database, and as a detailed 3-D volume in a harbor database—there is one instance of that light house in one database with linkage information to all images on which that light house was measured. With the re-adjustment process, even though that light house is measured on multiple images, only one set of ground coordinates is present for each extracted vertex of that light house. And, the error estimates of those vertexes are computed using the covariance propagated through the re-adjustment process. If more accurate imagery is acquired, the coordinates and error estimates are both updated to reflect the new information.
In an embodiment, a distributed adaptive geopositioning system comprises datastores of source imagery data, extracted information, and heritage information that are accessible via a network. In another embodiment, the analysis of the image and the extraction process are automated. In yet another embodiment, an automated distributed adaptive geopositioning system is used to provide alerts when changes are detected in selected features within an area of interest.